/**
* This file is part of ORB-SLAM.
*
* Copyright (C) 2014 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <http://webdiis.unizar.es/~raulmur/orbslam/>
*
* ORB-SLAM is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM. If not, see <http://www.gnu.org/licenses/>.
*/

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <vector>

#include "ORBextractor.hpp"


using namespace cv;
using namespace std;

namespace ORB_SLAM
{
    
    const float HARRIS_K = 0.04f;
    
    const int PATCH_SIZE = 31;
    const int HALF_PATCH_SIZE = 15;
    const int EDGE_THRESHOLD = 16;
    
    static void
    HarrisResponses(const Mat& img, vector<KeyPoint>& pts, int blockSize, float harris_k)
    {
        CV_Assert( img.type() == CV_8UC1 && blockSize*blockSize <= 2048 );
        
        size_t ptidx, ptsize = pts.size();
        
        const uchar* ptr00 = img.ptr<uchar>();
        int step = (int)(img.step/img.elemSize1());
        int r = blockSize/2;
        
        float scale = (1 << 2) * blockSize * 255.0f;
        scale = 1.0f / scale;
        float scale_sq_sq = scale * scale * scale * scale;
        
        AutoBuffer<int> ofsbuf(blockSize*blockSize);
        int* ofs = ofsbuf;
        for( int i = 0; i < blockSize; i++ )
            for( int j = 0; j < blockSize; j++ )
                ofs[i*blockSize + j] = (int)(i*step + j);
        
        for( ptidx = 0; ptidx < ptsize; ptidx++ )
        {
            int x0 = cvRound(pts[ptidx].pt.x - r);
            int y0 = cvRound(pts[ptidx].pt.y - r);
            
            const uchar* ptr0 = ptr00 + y0*step + x0;
            int a = 0, b = 0, c = 0;
            
            for( int k = 0; k < blockSize*blockSize; k++ )
            {
                const uchar* ptr = ptr0 + ofs[k];
                int Ix = (ptr[1] - ptr[-1])*2 + (ptr[-step+1] - ptr[-step-1]) + (ptr[step+1] - ptr[step-1]);
                int Iy = (ptr[step] - ptr[-step])*2 + (ptr[step-1] - ptr[-step-1]) + (ptr[step+1] - ptr[-step+1]);
                a += Ix*Ix;
                b += Iy*Iy;
                c += Ix*Iy;
            }
            pts[ptidx].response = ((float)a * b - (float)c * c -
                                   harris_k * ((float)a + b) * ((float)a + b))*scale_sq_sq;
        }
    }
    
    
    
    static float IC_Angle(const Mat& image, Point2f pt,  const vector<int> & u_max)
    {
        int m_01 = 0, m_10 = 0;
        
        const uchar* center = &image.at<uchar> (cvRound(pt.y), cvRound(pt.x));
        
        // Treat the center line differently, v=0
        for (int u = -HALF_PATCH_SIZE; u <= HALF_PATCH_SIZE; ++u)
            m_10 += u * center[u];
        
        // Go line by line in the circuI853lar patch
        int step = (int)image.step1();
        for (int v = 1; v <= HALF_PATCH_SIZE; ++v)
        {
            // Proceed over the two lines
            int v_sum = 0;
            int d = u_max[v];
            for (int u = -d; u <= d; ++u)
            {
                int val_plus = center[u + v*step], val_minus = center[u - v*step];
                v_sum += (val_plus - val_minus);
                m_10 += u * (val_plus + val_minus);
            }
            m_01 += v * v_sum;
        }
        
        return fastAtan2((float)m_01, (float)m_10);
    }
    
    
    const float factorPI = (float)(CV_PI/180.f);
    static void computeOrbDescriptor(const KeyPoint& kpt,
                                     const Mat& img, const Point* pattern,
                                     uchar* desc)
    {
        float angle = (float)kpt.angle*factorPI;
        float a = (float)cos(angle), b = (float)sin(angle);
        
        const uchar* center = &img.at<uchar>(cvRound(kpt.pt.y), cvRound(kpt.pt.x));
        const int step = (int)img.step;
        
#define GET_VALUE(idx) \
center[cvRound(pattern[idx].x*b + pattern[idx].y*a)*step + \
cvRound(pattern[idx].x*a - pattern[idx].y*b)]
        
        
        for (int i = 0; i < 32; ++i, pattern += 16)
        {
            int t0, t1, val;
            t0 = GET_VALUE(0); t1 = GET_VALUE(1);
            val = t0 < t1;
            t0 = GET_VALUE(2); t1 = GET_VALUE(3);
            val |= (t0 < t1) << 1;
            t0 = GET_VALUE(4); t1 = GET_VALUE(5);
            val |= (t0 < t1) << 2;
            t0 = GET_VALUE(6); t1 = GET_VALUE(7);
            val |= (t0 < t1) << 3;
            t0 = GET_VALUE(8); t1 = GET_VALUE(9);
            val |= (t0 < t1) << 4;
            t0 = GET_VALUE(10); t1 = GET_VALUE(11);
            val |= (t0 < t1) << 5;
            t0 = GET_VALUE(12); t1 = GET_VALUE(13);
            val |= (t0 < t1) << 6;
            t0 = GET_VALUE(14); t1 = GET_VALUE(15);
            val |= (t0 < t1) << 7;
            
            desc[i] = (uchar)val;
        }
        
#undef GET_VALUE
    }
    
    
    static int bit_pattern_31_[256*4] =
    {
        8,-3, 9,5/*mean (0), correlation (0)*/,
        4,2, 7,-12/*mean (1.12461e-05), correlation (0.0437584)*/,
        -11,9, -8,2/*mean (3.37382e-05), correlation (0.0617409)*/,
        7,-12, 12,-13/*mean (5.62303e-05), correlation (0.0636977)*/,
        2,-13, 2,12/*mean (0.000134953), correlation (0.085099)*/,
        1,-7, 1,6/*mean (0.000528565), correlation (0.0857175)*/,
        -2,-10, -2,-4/*mean (0.0188821), correlation (0.0985774)*/,
        -13,-13, -11,-8/*mean (0.0363135), correlation (0.0899616)*/,
        -13,-3, -12,-9/*mean (0.121806), correlation (0.099849)*/,
        10,4, 11,9/*mean (0.122065), correlation (0.093285)*/,
        -13,-8, -8,-9/*mean (0.162787), correlation (0.0942748)*/,
        -11,7, -9,12/*mean (0.21561), correlation (0.0974438)*/,
        7,7, 12,6/*mean (0.160583), correlation (0.130064)*/,
        -4,-5, -3,0/*mean (0.228171), correlation (0.132998)*/,
        -13,2, -12,-3/*mean (0.00997526), correlation (0.145926)*/,
        -9,0, -7,5/*mean (0.198234), correlation (0.143636)*/,
        12,-6, 12,-1/*mean (0.0676226), correlation (0.16689)*/,
        -3,6, -2,12/*mean (0.166847), correlation (0.171682)*/,
        -6,-13, -4,-8/*mean (0.101215), correlation (0.179716)*/,
        11,-13, 12,-8/*mean (0.200641), correlation (0.192279)*/,
        4,7, 5,1/*mean (0.205106), correlation (0.186848)*/,
        5,-3, 10,-3/*mean (0.234908), correlation (0.192319)*/,
        3,-7, 6,12/*mean (0.0709964), correlation (0.210872)*/,
        -8,-7, -6,-2/*mean (0.0939834), correlation (0.212589)*/,
        -2,11, -1,-10/*mean (0.127778), correlation (0.20866)*/,
        -13,12, -8,10/*mean (0.14783), correlation (0.206356)*/,
        -7,3, -5,-3/*mean (0.182141), correlation (0.198942)*/,
        -4,2, -3,7/*mean (0.188237), correlation (0.21384)*/,
        -10,-12, -6,11/*mean (0.14865), correlation (0.23571)*/,
        5,-12, 6,-7/*mean (0.222312), correlation (0.23324)*/,
        5,-6, 7,-1/*mean (0.229082), correlation (0.23389)*/,
        1,0, 4,-5/*mean (0.241577), correlation (0.215286)*/,
        9,11, 11,-13/*mean (0.00338507), correlation (0.251373)*/,
        4,7, 4,12/*mean (0.131005), correlation (0.257622)*/,
        2,-1, 4,4/*mean (0.152755), correlation (0.255205)*/,
        -4,-12, -2,7/*mean (0.182771), correlation (0.244867)*/,
        -8,-5, -7,-10/*mean (0.186898), correlation (0.23901)*/,
        4,11, 9,12/*mean (0.226226), correlation (0.258255)*/,
        0,-8, 1,-13/*mean (0.0897886), correlation (0.274827)*/,
        -13,-2, -8,2/*mean (0.148774), correlation (0.28065)*/,
        -3,-2, -2,3/*mean (0.153048), correlation (0.283063)*/,
        -6,9, -4,-9/*mean (0.169523), correlation (0.278248)*/,
        8,12, 10,7/*mean (0.225337), correlation (0.282851)*/,
        0,9, 1,3/*mean (0.226687), correlation (0.278734)*/,
        7,-5, 11,-10/*mean (0.00693882), correlation (0.305161)*/,
        -13,-6, -11,0/*mean (0.0227283), correlation (0.300181)*/,
        10,7, 12,1/*mean (0.125517), correlation (0.31089)*/,
        -6,-3, -6,12/*mean (0.131748), correlation (0.312779)*/,
        10,-9, 12,-4/*mean (0.144827), correlation (0.292797)*/,
        -13,8, -8,-12/*mean (0.149202), correlation (0.308918)*/,
        -13,0, -8,-4/*mean (0.160909), correlation (0.310013)*/,
        3,3, 7,8/*mean (0.177755), correlation (0.309394)*/,
        5,7, 10,-7/*mean (0.212337), correlation (0.310315)*/,
        -1,7, 1,-12/*mean (0.214429), correlation (0.311933)*/,
        3,-10, 5,6/*mean (0.235807), correlation (0.313104)*/,
        2,-4, 3,-10/*mean (0.00494827), correlation (0.344948)*/,
        -13,0, -13,5/*mean (0.0549145), correlation (0.344675)*/,
        -13,-7, -12,12/*mean (0.103385), correlation (0.342715)*/,
        -13,3, -11,8/*mean (0.134222), correlation (0.322922)*/,
        -7,12, -4,7/*mean (0.153284), correlation (0.337061)*/,
        6,-10, 12,8/*mean (0.154881), correlation (0.329257)*/,
        -9,-1, -7,-6/*mean (0.200967), correlation (0.33312)*/,
        -2,-5, 0,12/*mean (0.201518), correlation (0.340635)*/,
        -12,5, -7,5/*mean (0.207805), correlation (0.335631)*/,
        3,-10, 8,-13/*mean (0.224438), correlation (0.34504)*/,
        -7,-7, -4,5/*mean (0.239361), correlation (0.338053)*/,
        -3,-2, -1,-7/*mean (0.240744), correlation (0.344322)*/,
        2,9, 5,-11/*mean (0.242949), correlation (0.34145)*/,
        -11,-13, -5,-13/*mean (0.244028), correlation (0.336861)*/,
        -1,6, 0,-1/*mean (0.247571), correlation (0.343684)*/,
        5,-3, 5,2/*mean (0.000697256), correlation (0.357265)*/,
        -4,-13, -4,12/*mean (0.00213675), correlation (0.373827)*/,
        -9,-6, -9,6/*mean (0.0126856), correlation (0.373938)*/,
        -12,-10, -8,-4/*mean (0.0152497), correlation (0.364237)*/,
        10,2, 12,-3/*mean (0.0299933), correlation (0.345292)*/,
        7,12, 12,12/*mean (0.0307242), correlation (0.366299)*/,
        -7,-13, -6,5/*mean (0.0534975), correlation (0.368357)*/,
        -4,9, -3,4/*mean (0.099865), correlation (0.372276)*/,
        7,-1, 12,2/*mean (0.117083), correlation (0.364529)*/,
        -7,6, -5,1/*mean (0.126125), correlation (0.369606)*/,
        -13,11, -12,5/*mean (0.130364), correlation (0.358502)*/,
        -3,7, -2,-6/*mean (0.131691), correlation (0.375531)*/,
        7,-8, 12,-7/*mean (0.160166), correlation (0.379508)*/,
        -13,-7, -11,-12/*mean (0.167848), correlation (0.353343)*/,
        1,-3, 12,12/*mean (0.183378), correlation (0.371916)*/,
        2,-6, 3,0/*mean (0.228711), correlation (0.371761)*/,
        -4,3, -2,-13/*mean (0.247211), correlation (0.364063)*/,
        -1,-13, 1,9/*mean (0.249325), correlation (0.378139)*/,
        7,1, 8,-6/*mean (0.000652272), correlation (0.411682)*/,
        1,-1, 3,12/*mean (0.00248538), correlation (0.392988)*/,
        9,1, 12,6/*mean (0.0206815), correlation (0.386106)*/,
        -1,-9, -1,3/*mean (0.0364485), correlation (0.410752)*/,
        -13,-13, -10,5/*mean (0.0376068), correlation (0.398374)*/,
        7,7, 10,12/*mean (0.0424202), correlation (0.405663)*/,
        12,-5, 12,9/*mean (0.0942645), correlation (0.410422)*/,
        6,3, 7,11/*mean (0.1074), correlation (0.413224)*/,
        5,-13, 6,10/*mean (0.109256), correlation (0.408646)*/,
        2,-12, 2,3/*mean (0.131691), correlation (0.416076)*/,
        3,8, 4,-6/*mean (0.165081), correlation (0.417569)*/,
        2,6, 12,-13/*mean (0.171874), correlation (0.408471)*/,
        9,-12, 10,3/*mean (0.175146), correlation (0.41296)*/,
        -8,4, -7,9/*mean (0.183682), correlation (0.402956)*/,
        -11,12, -4,-6/*mean (0.184672), correlation (0.416125)*/,
        1,12, 2,-8/*mean (0.191487), correlation (0.386696)*/,
        6,-9, 7,-4/*mean (0.192668), correlation (0.394771)*/,
        2,3, 3,-2/*mean (0.200157), correlation (0.408303)*/,
        6,3, 11,0/*mean (0.204588), correlation (0.411762)*/,
        3,-3, 8,-8/*mean (0.205904), correlation (0.416294)*/,
        7,8, 9,3/*mean (0.213237), correlation (0.409306)*/,
        -11,-5, -6,-4/*mean (0.243444), correlation (0.395069)*/,
        -10,11, -5,10/*mean (0.247672), correlation (0.413392)*/,
        -5,-8, -3,12/*mean (0.24774), correlation (0.411416)*/,
        -10,5, -9,0/*mean (0.00213675), correlation (0.454003)*/,
        8,-1, 12,-6/*mean (0.0293635), correlation (0.455368)*/,
        4,-6, 6,-11/*mean (0.0404971), correlation (0.457393)*/,
        -10,12, -8,7/*mean (0.0481107), correlation (0.448364)*/,
        4,-2, 6,7/*mean (0.050641), correlation (0.455019)*/,
        -2,0, -2,12/*mean (0.0525978), correlation (0.44338)*/,
        -5,-8, -5,2/*mean (0.0629667), correlation (0.457096)*/,
        7,-6, 10,12/*mean (0.0653846), correlation (0.445623)*/,
        -9,-13, -8,-8/*mean (0.0858749), correlation (0.449789)*/,
        -5,-13, -5,-2/*mean (0.122402), correlation (0.450201)*/,
        8,-8, 9,-13/*mean (0.125416), correlation (0.453224)*/,
        -9,-11, -9,0/*mean (0.130128), correlation (0.458724)*/,
        1,-8, 1,-2/*mean (0.132467), correlation (0.440133)*/,
        7,-4, 9,1/*mean (0.132692), correlation (0.454)*/,
        -2,1, -1,-4/*mean (0.135695), correlation (0.455739)*/,
        11,-6, 12,-11/*mean (0.142904), correlation (0.446114)*/,
        -12,-9, -6,4/*mean (0.146165), correlation (0.451473)*/,
        3,7, 7,12/*mean (0.147627), correlation (0.456643)*/,
        5,5, 10,8/*mean (0.152901), correlation (0.455036)*/,
        0,-4, 2,8/*mean (0.167083), correlation (0.459315)*/,
        -9,12, -5,-13/*mean (0.173234), correlation (0.454706)*/,
        0,7, 2,12/*mean (0.18312), correlation (0.433855)*/,
        -1,2, 1,7/*mean (0.185504), correlation (0.443838)*/,
        5,11, 7,-9/*mean (0.185706), correlation (0.451123)*/,
        3,5, 6,-8/*mean (0.188968), correlation (0.455808)*/,
        -13,-4, -8,9/*mean (0.191667), correlation (0.459128)*/,
        -5,9, -3,-3/*mean (0.193196), correlation (0.458364)*/,
        -4,-7, -3,-12/*mean (0.196536), correlation (0.455782)*/,
        6,5, 8,0/*mean (0.1972), correlation (0.450481)*/,
        -7,6, -6,12/*mean (0.199438), correlation (0.458156)*/,
        -13,6, -5,-2/*mean (0.211224), correlation (0.449548)*/,
        1,-10, 3,10/*mean (0.211718), correlation (0.440606)*/,
        4,1, 8,-4/*mean (0.213034), correlation (0.443177)*/,
        -2,-2, 2,-13/*mean (0.234334), correlation (0.455304)*/,
        2,-12, 12,12/*mean (0.235684), correlation (0.443436)*/,
        -2,-13, 0,-6/*mean (0.237674), correlation (0.452525)*/,
        4,1, 9,3/*mean (0.23962), correlation (0.444824)*/,
        -6,-10, -3,-5/*mean (0.248459), correlation (0.439621)*/,
        -3,-13, -1,1/*mean (0.249505), correlation (0.456666)*/,
        7,5, 12,-11/*mean (0.00119208), correlation (0.495466)*/,
        4,-2, 5,-7/*mean (0.00372245), correlation (0.484214)*/,
        -13,9, -9,-5/*mean (0.00741116), correlation (0.499854)*/,
        7,1, 8,6/*mean (0.0208952), correlation (0.499773)*/,
        7,-8, 7,6/*mean (0.0220085), correlation (0.501609)*/,
        -7,-4, -7,1/*mean (0.0233806), correlation (0.496568)*/,
        -8,11, -7,-8/*mean (0.0236505), correlation (0.489719)*/,
        -13,6, -12,-8/*mean (0.0268781), correlation (0.503487)*/,
        2,4, 3,9/*mean (0.0323324), correlation (0.501938)*/,
        10,-5, 12,3/*mean (0.0399235), correlation (0.494029)*/,
        -6,-5, -6,7/*mean (0.0420153), correlation (0.486579)*/,
        8,-3, 9,-8/*mean (0.0548021), correlation (0.484237)*/,
        2,-12, 2,8/*mean (0.0616622), correlation (0.496642)*/,
        -11,-2, -10,3/*mean (0.0627755), correlation (0.498563)*/,
        -12,-13, -7,-9/*mean (0.0829622), correlation (0.495491)*/,
        -11,0, -10,-5/*mean (0.0843342), correlation (0.487146)*/,
        5,-3, 11,8/*mean (0.0929937), correlation (0.502315)*/,
        -2,-13, -1,12/*mean (0.113327), correlation (0.48941)*/,
        -1,-8, 0,9/*mean (0.132119), correlation (0.467268)*/,
        -13,-11, -12,-5/*mean (0.136269), correlation (0.498771)*/,
        -10,-2, -10,11/*mean (0.142173), correlation (0.498714)*/,
        -3,9, -2,-13/*mean (0.144141), correlation (0.491973)*/,
        2,-3, 3,2/*mean (0.14892), correlation (0.500782)*/,
        -9,-13, -4,0/*mean (0.150371), correlation (0.498211)*/,
        -4,6, -3,-10/*mean (0.152159), correlation (0.495547)*/,
        -4,12, -2,-7/*mean (0.156152), correlation (0.496925)*/,
        -6,-11, -4,9/*mean (0.15749), correlation (0.499222)*/,
        6,-3, 6,11/*mean (0.159211), correlation (0.503821)*/,
        -13,11, -5,5/*mean (0.162427), correlation (0.501907)*/,
        11,11, 12,6/*mean (0.16652), correlation (0.497632)*/,
        7,-5, 12,-2/*mean (0.169141), correlation (0.484474)*/,
        -1,12, 0,7/*mean (0.169456), correlation (0.495339)*/,
        -4,-8, -3,-2/*mean (0.171457), correlation (0.487251)*/,
        -7,1, -6,7/*mean (0.175), correlation (0.500024)*/,
        -13,-12, -8,-13/*mean (0.175866), correlation (0.497523)*/,
        -7,-2, -6,-8/*mean (0.178273), correlation (0.501854)*/,
        -8,5, -6,-9/*mean (0.181107), correlation (0.494888)*/,
        -5,-1, -4,5/*mean (0.190227), correlation (0.482557)*/,
        -13,7, -8,10/*mean (0.196739), correlation (0.496503)*/,
        1,5, 5,-13/*mean (0.19973), correlation (0.499759)*/,
        1,0, 10,-13/*mean (0.204465), correlation (0.49873)*/,
        9,12, 10,-1/*mean (0.209334), correlation (0.49063)*/,
        5,-8, 10,-9/*mean (0.211134), correlation (0.503011)*/,
        -1,11, 1,-13/*mean (0.212), correlation (0.499414)*/,
        -9,-3, -6,2/*mean (0.212168), correlation (0.480739)*/,
        -1,-10, 1,12/*mean (0.212731), correlation (0.502523)*/,
        -13,1, -8,-10/*mean (0.21327), correlation (0.489786)*/,
        8,-11, 10,-6/*mean (0.214159), correlation (0.488246)*/,
        2,-13, 3,-6/*mean (0.216993), correlation (0.50287)*/,
        7,-13, 12,-9/*mean (0.223639), correlation (0.470502)*/,
        -10,-10, -5,-7/*mean (0.224089), correlation (0.500852)*/,
        -10,-8, -8,-13/*mean (0.228666), correlation (0.502629)*/,
        4,-6, 8,5/*mean (0.22906), correlation (0.498305)*/,
        3,12, 8,-13/*mean (0.233378), correlation (0.503825)*/,
        -4,2, -3,-3/*mean (0.234323), correlation (0.476692)*/,
        5,-13, 10,-12/*mean (0.236392), correlation (0.475462)*/,
        4,-13, 5,-1/*mean (0.236842), correlation (0.504132)*/,
        -9,9, -4,3/*mean (0.236977), correlation (0.497739)*/,
        0,3, 3,-9/*mean (0.24314), correlation (0.499398)*/,
        -12,1, -6,1/*mean (0.243297), correlation (0.489447)*/,
        3,2, 4,-8/*mean (0.00155196), correlation (0.553496)*/,
        -10,-10, -10,9/*mean (0.00239541), correlation (0.54297)*/,
        8,-13, 12,12/*mean (0.0034413), correlation (0.544361)*/,
        -8,-12, -6,-5/*mean (0.003565), correlation (0.551225)*/,
        2,2, 3,7/*mean (0.00835583), correlation (0.55285)*/,
        10,6, 11,-8/*mean (0.00885065), correlation (0.540913)*/,
        6,8, 8,-12/*mean (0.0101552), correlation (0.551085)*/,
        -7,10, -6,5/*mean (0.0102227), correlation (0.533635)*/,
        -3,-9, -3,9/*mean (0.0110211), correlation (0.543121)*/,
        -1,-13, -1,5/*mean (0.0113473), correlation (0.550173)*/,
        -3,-7, -3,4/*mean (0.0140913), correlation (0.554774)*/,
        -8,-2, -8,3/*mean (0.017049), correlation (0.55461)*/,
        4,2, 12,12/*mean (0.01778), correlation (0.546921)*/,
        2,-5, 3,11/*mean (0.0224022), correlation (0.549667)*/,
        6,-9, 11,-13/*mean (0.029161), correlation (0.546295)*/,
        3,-1, 7,12/*mean (0.0303081), correlation (0.548599)*/,
        11,-1, 12,4/*mean (0.0355151), correlation (0.523943)*/,
        -3,0, -3,6/*mean (0.0417904), correlation (0.543395)*/,
        4,-11, 4,12/*mean (0.0487292), correlation (0.542818)*/,
        2,-4, 2,1/*mean (0.0575124), correlation (0.554888)*/,
        -10,-6, -8,1/*mean (0.0594242), correlation (0.544026)*/,
        -13,7, -11,1/*mean (0.0597391), correlation (0.550524)*/,
        -13,12, -11,-13/*mean (0.0608974), correlation (0.55383)*/,
        6,0, 11,-13/*mean (0.065126), correlation (0.552006)*/,
        0,-1, 1,4/*mean (0.074224), correlation (0.546372)*/,
        -13,3, -9,-2/*mean (0.0808592), correlation (0.554875)*/,
        -9,8, -6,-3/*mean (0.0883378), correlation (0.551178)*/,
        -13,-6, -8,-2/*mean (0.0901035), correlation (0.548446)*/,
        5,-9, 8,10/*mean (0.0949843), correlation (0.554694)*/,
        2,7, 3,-9/*mean (0.0994152), correlation (0.550979)*/,
        -1,-6, -1,-1/*mean (0.10045), correlation (0.552714)*/,
        9,5, 11,-2/*mean (0.100686), correlation (0.552594)*/,
        11,-3, 12,-8/*mean (0.101091), correlation (0.532394)*/,
        3,0, 3,5/*mean (0.101147), correlation (0.525576)*/,
        -1,4, 0,10/*mean (0.105263), correlation (0.531498)*/,
        3,-6, 4,5/*mean (0.110785), correlation (0.540491)*/,
        -13,0, -10,5/*mean (0.112798), correlation (0.536582)*/,
        5,8, 12,11/*mean (0.114181), correlation (0.555793)*/,
        8,9, 9,-6/*mean (0.117431), correlation (0.553763)*/,
        7,-4, 8,-12/*mean (0.118522), correlation (0.553452)*/,
        -10,4, -10,9/*mean (0.12094), correlation (0.554785)*/,
        7,3, 12,4/*mean (0.122582), correlation (0.555825)*/,
        9,-7, 10,-2/*mean (0.124978), correlation (0.549846)*/,
        7,0, 12,-2/*mean (0.127002), correlation (0.537452)*/,
        -1,-6, 0,-11/*mean (0.127148), correlation (0.547401)*/
    };
    
    ORBextractor::ORBextractor(int _nfeatures, float _scaleFactor, int _nlevels, int _scoreType,
                               int _fastTh):
    nfeatures(_nfeatures), scaleFactor(_scaleFactor), nlevels(_nlevels),
    scoreType(_scoreType), fastTh(_fastTh)
    {
        mvScaleFactor.resize(nlevels);
        mvScaleFactor[0]=1;
        for(int i=1; i<nlevels; i++)
            mvScaleFactor[i]=mvScaleFactor[i-1]*scaleFactor;
        
        float invScaleFactor = 1.0f/scaleFactor;
        mvInvScaleFactor.resize(nlevels);
        mvInvScaleFactor[0]=1;
        for(int i=1; i<nlevels; i++)
            mvInvScaleFactor[i]=mvInvScaleFactor[i-1]*invScaleFactor;
        
        mvImagePyramid.resize(nlevels);
        mvMaskPyramid.resize(nlevels);
        
        mnFeaturesPerLevel.resize(nlevels);
        float factor = (float)(1.0 / scaleFactor);
        float nDesiredFeaturesPerScale = nfeatures*(1 - factor)/(1 - (float)pow((double)factor, (double)nlevels));
        
        int sumFeatures = 0;
        for( int level = 0; level < nlevels-1; level++ )
        {
            mnFeaturesPerLevel[level] = cvRound(nDesiredFeaturesPerScale);
            sumFeatures += mnFeaturesPerLevel[level];
            nDesiredFeaturesPerScale *= factor;
        }
        mnFeaturesPerLevel[nlevels-1] = std::max(nfeatures - sumFeatures, 0);
        
        const int npoints = 512;
        const Point* pattern0 = (const Point*)bit_pattern_31_;
        std::copy(pattern0, pattern0 + npoints, std::back_inserter(pattern));
        
        //This is for orientation
        // pre-compute the end of a row in a circular patch
        umax.resize(HALF_PATCH_SIZE + 1);
        
        int v, v0, vmax = cvFloor(HALF_PATCH_SIZE * sqrt(2.f) / 2 + 1);
        int vmin = cvCeil(HALF_PATCH_SIZE * sqrt(2.f) / 2);
        const double hp2 = HALF_PATCH_SIZE*HALF_PATCH_SIZE;
        for (v = 0; v <= vmax; ++v)
            umax[v] = cvRound(sqrt(hp2 - v * v));
        
        // Make sure we are symmetric
        for (v = HALF_PATCH_SIZE, v0 = 0; v >= vmin; --v)
        {
            while (umax[v0] == umax[v0 + 1])
                ++v0;
            umax[v] = v0;
            ++v0;
        }
    }
    
    static void computeOrientation(const Mat& image, vector<KeyPoint>& keypoints, const vector<int>& umax)
    {
        for (vector<KeyPoint>::iterator keypoint = keypoints.begin(),
             keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
        {
            keypoint->angle = IC_Angle(image, keypoint->pt, umax);
        }
    }
    
    void ORBextractor::ComputeKeyPoints(vector<vector<KeyPoint> >& allKeypoints)
    {
        
        allKeypoints.resize(nlevels);
        
        float imageRatio = (float)mvImagePyramid[0].cols/mvImagePyramid[0].rows;
        
        for (int level = 0; level < nlevels; ++level)
        {
            const int nDesiredFeatures = mnFeaturesPerLevel[level];
            
            const int levelCols = sqrt((float)nDesiredFeatures/(5*imageRatio));
            const int levelRows = imageRatio*levelCols;
            
            const int minBorderX = EDGE_THRESHOLD;
            const int minBorderY = minBorderX;
            const int maxBorderX = mvImagePyramid[level].cols-EDGE_THRESHOLD;
            const int maxBorderY = mvImagePyramid[level].rows-EDGE_THRESHOLD;
            
            const int W = maxBorderX - minBorderX;
            const int H = maxBorderY - minBorderY;
            const int cellW = ceil((float)W/levelCols);
            const int cellH = ceil((float)H/levelRows);
            
            const int nCells = levelRows*levelCols;
            const int nfeaturesCell = ceil((float)nDesiredFeatures/nCells);
            
            vector<vector<vector<KeyPoint> > > cellKeyPoints(levelRows, vector<vector<KeyPoint> >(levelCols));
            
            vector<vector<int> > nToRetain(levelRows,vector<int>(levelCols));
            vector<vector<int> > nTotal(levelRows,vector<int>(levelCols));
            vector<vector<bool> > bNoMore(levelRows,vector<bool>(levelCols,false));
            vector<int> iniXCol(levelCols);
            vector<int> iniYRow(levelRows);
            int nNoMore = 0;
            int nToDistribute = 0;
            
            
            float hY = cellH + 6;
            
            for(int i=0; i<levelRows; i++)
            {
                const float iniY = minBorderY + i*cellH - 3;
                iniYRow[i] = iniY;
                
                if(i == levelRows-1)
                {
                    hY = maxBorderY+3-iniY;
                    if(hY<=0)
                        continue;
                }
                
                float hX = cellW + 6;
                
                for(int j=0; j<levelCols; j++)
                {
                    float iniX;
                    
                    if(i==0)
                    {
                        iniX = minBorderX + j*cellW - 3;
                        iniXCol[j] = iniX;
                    }
                    else
                    {
                        iniX = iniXCol[j];
                    }
                    
                    
                    if(j == levelCols-1)
                    {
                        hX = maxBorderX+3-iniX;
                        if(hX<=0)
                            continue;
                    }
                    
                    
                    Mat cellImage = mvImagePyramid[level].rowRange(iniY,iniY+hY).colRange(iniX,iniX+hX);
                    
                    Mat cellMask;
                    if(!mvMaskPyramid[level].empty())
                        cellMask = cv::Mat(mvMaskPyramid[level],Rect(iniX,iniY,hX,hY));
                    
                    cellKeyPoints[i][j].reserve(nfeaturesCell*5);
                    
                    FAST(cellImage,cellKeyPoints[i][j],fastTh,true);
                    
                    if(cellKeyPoints[i][j].size()<=3)
                    {
                        cellKeyPoints[i][j].clear();
                        
                        FAST(cellImage,cellKeyPoints[i][j],7,true);
                    }
                    
                    if( scoreType == ORB::HARRIS_SCORE )
                    {
                        // Compute the Harris cornerness
                        HarrisResponses(cellImage,cellKeyPoints[i][j], 7, HARRIS_K);
                    }
                    
                    const int nKeys = cellKeyPoints[i][j].size();
                    nTotal[i][j] = nKeys;
                    
                    if(nKeys>nfeaturesCell)
                    {
                        nToRetain[i][j] = nfeaturesCell;
                        bNoMore[i][j] = false;
                    }
                    else
                    {
                        nToRetain[i][j] = nKeys;
                        nToDistribute += nfeaturesCell-nKeys;
                        bNoMore[i][j] = true;
                        nNoMore++;
                    }
                    
                }
            }
            
            
            // Retain by score
            
            while(nToDistribute>0 && nNoMore<nCells)
            {
                int nNewFeaturesCell = nfeaturesCell + ceil((float)nToDistribute/(nCells-nNoMore));
                nToDistribute = 0;
                
                for(int i=0; i<levelRows; i++)
                {
                    for(int j=0; j<levelCols; j++)
                    {
                        if(!bNoMore[i][j])
                        {
                            if(nTotal[i][j]>nNewFeaturesCell)
                            {
                                nToRetain[i][j] = nNewFeaturesCell;
                                bNoMore[i][j] = false;
                            }
                            else
                            {
                                nToRetain[i][j] = nTotal[i][j];
                                nToDistribute += nNewFeaturesCell-nTotal[i][j];
                                bNoMore[i][j] = true;
                                nNoMore++;
                            }
                        }
                    }
                }
            }
            
            vector<KeyPoint> & keypoints = allKeypoints[level];
            keypoints.reserve(nDesiredFeatures*2);
            
            const int scaledPatchSize = PATCH_SIZE*mvScaleFactor[level];
            
            // Retain by score and transform coordinates
            for(int i=0; i<levelRows; i++)
            {
                for(int j=0; j<levelCols; j++)
                {
                    vector<KeyPoint> &keysCell = cellKeyPoints[i][j];
                    KeyPointsFilter::retainBest(keysCell,nToRetain[i][j]);
                    if((int)keysCell.size()>nToRetain[i][j])
                        keysCell.resize(nToRetain[i][j]);
                    
                    for(size_t k=0, kend=keysCell.size(); k<kend; k++)
                    {
                        keysCell[k].pt.x+=iniXCol[j];
                        keysCell[k].pt.y+=iniYRow[i];
                        keysCell[k].octave=level;
                        keysCell[k].size = scaledPatchSize;
                        keypoints.push_back(keysCell[k]);
                    }
                }
            }
            if((int)keypoints.size()>nDesiredFeatures)
            {
                KeyPointsFilter::retainBest(keypoints,nDesiredFeatures);
                keypoints.resize(nDesiredFeatures);
            }
        }
        
        // and compute orientations
        for (int level = 0; level < nlevels; ++level)
            computeOrientation(mvImagePyramid[level], allKeypoints[level], umax);
    }
    
    static void computeDescriptors(const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors,
                                   const vector<Point>& pattern)
    {
        descriptors = Mat::zeros((int)keypoints.size(), 32, CV_8UC1);
        
        for (size_t i = 0; i < keypoints.size(); i++)
            computeOrbDescriptor(keypoints[i], image, &pattern[0], descriptors.ptr((int)i));
    }
    
    void ORBextractor::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _keypoints,
                                  OutputArray _descriptors)
    {
        if(_image.empty())
            return;
        
        Mat image = _image.getMat(), mask = _mask.getMat();
        assert(image.type() == CV_8UC1 );
        
        // Pre-compute the scale pyramids
        ComputePyramid(image, mask);
        
        vector < vector<KeyPoint> > allKeypoints;
        ComputeKeyPoints(allKeypoints);
        
        Mat descriptors;
        
        int nkeypoints = 0;
        for (int level = 0; level < nlevels; ++level)
            nkeypoints += (int)allKeypoints[level].size();
        if( nkeypoints == 0 )
            _descriptors.release();
        else
        {
            _descriptors.create(nkeypoints, 32, CV_8U);
            descriptors = _descriptors.getMat();
        }
        
        _keypoints.clear();
        _keypoints.reserve(nkeypoints);
        
        int offset = 0;
        for (int level = 0; level < nlevels; ++level)
        {
            vector<KeyPoint>& keypoints = allKeypoints[level];
            int nkeypointsLevel = (int)keypoints.size();
            
            if(nkeypointsLevel==0)
                continue;
            
            // preprocess the resized image
            Mat& workingMat = mvImagePyramid[level];
            GaussianBlur(workingMat, workingMat, Size(7, 7), 2, 2, BORDER_REFLECT_101);
            
            // Compute the descriptors
            Mat desc = descriptors.rowRange(offset, offset + nkeypointsLevel);
            computeDescriptors(workingMat, keypoints, desc, pattern);
            
            offset += nkeypointsLevel;
            
            // Scale keypoint coordinates
            if (level != 0)
            {
                float scale = mvScaleFactor[level]; //getScale(level, firstLevel, scaleFactor);
                for (vector<KeyPoint>::iterator keypoint = keypoints.begin(),
                     keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
                    keypoint->pt *= scale;
            }
            // And add the keypoints to the output
            _keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end());
        }
    }
    
    void ORBextractor::ComputePyramid(cv::Mat image, cv::Mat Mask)
    {
        for (int level = 0; level < nlevels; ++level)
        {
            float scale = mvInvScaleFactor[level];
            Size sz(cvRound((float)image.cols*scale), cvRound((float)image.rows*scale));
            Size wholeSize(sz.width + EDGE_THRESHOLD*2, sz.height + EDGE_THRESHOLD*2);
            Mat temp(wholeSize, image.type()), masktemp;
            mvImagePyramid[level] = temp(Rect(EDGE_THRESHOLD, EDGE_THRESHOLD, sz.width, sz.height));
            
            if( !Mask.empty() )
            {
                masktemp = Mat(wholeSize, Mask.type());
                mvMaskPyramid[level] = masktemp(Rect(EDGE_THRESHOLD, EDGE_THRESHOLD, sz.width, sz.height));
            }
            
            // Compute the resized image
            if( level != 0 )
            {
                resize(mvImagePyramid[level-1], mvImagePyramid[level], sz, 0, 0, INTER_LINEAR);
                if (!Mask.empty())
                {
                    resize(mvMaskPyramid[level-1], mvMaskPyramid[level], sz, 0, 0, INTER_NEAREST);
                }
                
                copyMakeBorder(mvImagePyramid[level], temp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
                               BORDER_REFLECT_101+BORDER_ISOLATED);
                if (!Mask.empty())
                    copyMakeBorder(mvMaskPyramid[level], masktemp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
                                   BORDER_CONSTANT+BORDER_ISOLATED);
            }
            else
            {
                copyMakeBorder(image, temp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
                               BORDER_REFLECT_101);
                if( !Mask.empty() )
                    copyMakeBorder(Mask, masktemp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
                                   BORDER_CONSTANT+BORDER_ISOLATED);
            }
        }
        
    }
    
} //namespace ORB_SLAM
